{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b90a1267",
   "metadata": {},
   "source": [
    "# ResNet 10 与 CIFAR10 dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d864c3e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torchvision\n",
    "from torchvision import models, transforms\n",
    "from torchinfo import summary\n",
    "from tqdm import tqdm\n",
    "from torchvision.models import resnet\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "906db928",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "==========================================================================================\nLayer (type:depth-idx)                   Output Shape              Param #\n==========================================================================================\nResNet                                   --                        --\n├─Conv2d: 1-1                            [1, 64, 16, 16]           9,408\n├─BatchNorm2d: 1-2                       [1, 64, 16, 16]           128\n├─ReLU: 1-3                              [1, 64, 16, 16]           --\n├─MaxPool2d: 1-4                         [1, 64, 8, 8]             --\n├─Sequential: 1-5                        [1, 64, 8, 8]             --\n│    └─BasicBlock: 2-1                   [1, 64, 8, 8]             --\n│    │    └─Conv2d: 3-1                  [1, 64, 8, 8]             36,864\n│    │    └─BatchNorm2d: 3-2             [1, 64, 8, 8]             128\n│    │    └─ReLU: 3-3                    [1, 64, 8, 8]             --\n│    │    └─Conv2d: 3-4                  [1, 64, 8, 8]             36,864\n│    │    └─BatchNorm2d: 3-5             [1, 64, 8, 8]             128\n│    │    └─ReLU: 3-6                    [1, 64, 8, 8]             --\n├─Sequential: 1-6                        [1, 128, 4, 4]            --\n│    └─BasicBlock: 2-2                   [1, 128, 4, 4]            --\n│    │    └─Conv2d: 3-7                  [1, 128, 4, 4]            73,728\n│    │    └─BatchNorm2d: 3-8             [1, 128, 4, 4]            256\n│    │    └─ReLU: 3-9                    [1, 128, 4, 4]            --\n│    │    └─Conv2d: 3-10                 [1, 128, 4, 4]            147,456\n│    │    └─BatchNorm2d: 3-11            [1, 128, 4, 4]            256\n│    │    └─Sequential: 3-12             [1, 128, 4, 4]            8,448\n│    │    └─ReLU: 3-13                   [1, 128, 4, 4]            --\n├─Sequential: 1-7                        [1, 256, 2, 2]            --\n│    └─BasicBlock: 2-3                   [1, 256, 2, 2]            --\n│    │    └─Conv2d: 3-14                 [1, 256, 2, 2]            294,912\n│    │    └─BatchNorm2d: 3-15            [1, 256, 2, 2]            512\n│    │    └─ReLU: 3-16                   [1, 256, 2, 2]            --\n│    │    └─Conv2d: 3-17                 [1, 256, 2, 2]            589,824\n│    │    └─BatchNorm2d: 3-18            [1, 256, 2, 2]            512\n│    │    └─Sequential: 3-19             [1, 256, 2, 2]            33,280\n│    │    └─ReLU: 3-20                   [1, 256, 2, 2]            --\n├─Sequential: 1-8                        [1, 512, 1, 1]            --\n│    └─BasicBlock: 2-4                   [1, 512, 1, 1]            --\n│    │    └─Conv2d: 3-21                 [1, 512, 1, 1]            1,179,648\n│    │    └─BatchNorm2d: 3-22            [1, 512, 1, 1]            1,024\n│    │    └─ReLU: 3-23                   [1, 512, 1, 1]            --\n│    │    └─Conv2d: 3-24                 [1, 512, 1, 1]            2,359,296\n│    │    └─BatchNorm2d: 3-25            [1, 512, 1, 1]            1,024\n│    │    └─Sequential: 3-26             [1, 512, 1, 1]            132,096\n│    │    └─ReLU: 3-27                   [1, 512, 1, 1]            --\n├─AdaptiveAvgPool2d: 1-9                 [1, 512, 1, 1]            --\n├─Linear: 1-10                           [1, 10]                   5,130\n==========================================================================================\nTotal params: 4,910,922\nTrainable params: 4,910,922\nNon-trainable params: 0\nTotal mult-adds (M): 18.15\n==========================================================================================\nInput size (MB): 0.01\nForward/backward pass size (MB): 0.57\nParams size (MB): 19.64\nEstimated Total Size (MB): 20.22\n=========================================================================================="
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 定义是否使用GPU\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "trans = transforms.Compose((transforms.Resize(32), transforms.ToTensor()))\n",
    "# 要对从cifar中下载的图像进行一些转变，所以这里先初始化transform\n",
    "batch_size = 30\n",
    "trainset = torchvision.datasets.CIFAR10(\n",
    "    root=r'D:/Data',\n",
    "    train=True,\n",
    "    download=False,\n",
    "    transform=trans)  # 训练数据集\n",
    "trainloader = torch.utils.data.DataLoader(\n",
    "    trainset, batch_size=batch_size, shuffle=True,\n",
    "    num_workers=2)  # 生成一个个batch进行批训练，组成batch的时候顺序打乱取\n",
    "\n",
    "PATH = './resnet_cifar_net.pth'\n",
    "# 模型定义-ResNet\n",
    "net = models.resnet.ResNet(resnet.BasicBlock, [1, 1, 1, 1],  num_classes=10, )\n",
    "summary(net, (1, 3, 32, 32))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c9f222c",
   "metadata": {},
   "source": [
    "## 开始训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "135e75ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "training in Epoch 0/10: 100%|██████████| 1667/1667 [00:27<00:00, 61.23it/s, Loss=1.35, train_acc=0.52] \n",
      "training in Epoch 1/10: 100%|██████████| 1667/1667 [00:27<00:00, 61.05it/s, Loss=0.972, train_acc=0.659]\n",
      "training in Epoch 2/10: 100%|██████████| 1667/1667 [00:26<00:00, 63.03it/s, Loss=0.815, train_acc=0.715]\n",
      "training in Epoch 3/10: 100%|██████████| 1667/1667 [00:26<00:00, 63.40it/s, Loss=0.694, train_acc=0.758]\n",
      "training in Epoch 4/10: 100%|██████████| 1667/1667 [00:26<00:00, 62.49it/s, Loss=0.589, train_acc=0.795]\n",
      "training in Epoch 5/10: 100%|██████████| 1667/1667 [00:27<00:00, 60.64it/s, Loss=0.494, train_acc=0.828]\n",
      "training in Epoch 6/10: 100%|██████████| 1667/1667 [00:27<00:00, 60.12it/s, Loss=0.403, train_acc=0.86] \n",
      "training in Epoch 7/10: 100%|██████████| 1667/1667 [00:27<00:00, 60.36it/s, Loss=0.332, train_acc=0.884]\n",
      "training in Epoch 8/10: 100%|██████████| 1667/1667 [00:27<00:00, 60.26it/s, Loss=0.27, train_acc=0.905] \n",
      "training in Epoch 9/10: 100%|██████████| 1667/1667 [00:28<00:00, 59.33it/s, Loss=0.224, train_acc=0.921]\n"
     ]
    }
   ],
   "source": [
    "net = net.to(device)\n",
    "\n",
    "# 定义损失函数和优化方式\n",
    "loss_fn = nn.CrossEntropyLoss().to(device)  # 损失函数为交叉熵，多用于多分类问题\n",
    "optimizer = torch.optim.Adam(net.parameters(), lr=1e-3)  # 选好优化方式\n",
    "\n",
    "num_epochs = 10\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    running_loss = 0\n",
    "    running_correct = 0\n",
    "    net.train()\n",
    "    # 训练共50000张图片， batch_size=30, 每个batch有1667个数据\n",
    "    with tqdm(trainloader, desc=\"training in Epoch {}/{}\".format(epoch, num_epochs),) as tq:\n",
    "        for batchidx, (x, labels) in enumerate(tq):\n",
    "            x, labels = x.to(device), labels.to(device)\n",
    "            outputs = net.forward(x)\n",
    "            _, preds = torch.max(outputs.data, 1)\n",
    "\n",
    "            loss = loss_fn(outputs, labels)\n",
    "\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "            running_loss += loss.item()\n",
    "            running_correct += torch.sum(preds == labels.data).item()\n",
    "\n",
    "            epoch_loss = running_loss / (batchidx + 1)\n",
    "            epoch_acc = running_correct / (batchidx * batch_size + x.size(0))\n",
    "            tq.set_postfix(Loss=epoch_loss, train_acc=epoch_acc, )\n",
    "\n",
    "torch.save(net.state_dict(), PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4a46fe6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "from torchvision import models, transforms\n",
    "from tqdm import tqdm\n",
    "from torchvision.models import resnet\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "47e242d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "testing : 100%|██████████| 334/334 [00:03<00:00, 93.44it/s] \n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "trans = transforms.Compose((transforms.Resize(32), transforms.ToTensor()))\n",
    "PATH = './resnet_cifar_net.pth'\n",
    "\n",
    "testset = torchvision.datasets.CIFAR10(\n",
    "        root=r'D:/data',\n",
    "        train=False,\n",
    "        download=False,\n",
    "        transform=trans)\n",
    "testloader = torch.utils.data.DataLoader(testset,\n",
    "                                         batch_size=30,\n",
    "                                         shuffle=True,\n",
    "                                         num_workers=2)\n",
    "# ResNet 10\n",
    "resnet = models.resnet.ResNet(resnet.BasicBlock, [1, 1, 1, 1],  num_classes=10, )\n",
    "resnet.load_state_dict(torch.load(PATH))\n",
    "resnet = resnet.to(device)\n",
    "\n",
    "resnet.eval()\n",
    "with torch.no_grad():\n",
    "    y_labels = []\n",
    "    y_preds = []\n",
    "    with tqdm(testloader, desc=\"testing \", ) as tq:\n",
    "        #  测试集一共10000张图片，batch_size=30 ,所以一共有334个batch\n",
    "        for images, labels in tq:\n",
    "            images, labels = images.to(device), labels.to(device)\n",
    "            outputs = resnet(images)\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "\n",
    "            y_labels.extend(labels.cpu().data.numpy())\n",
    "            y_preds.extend(predicted.cpu().data.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "694d0aef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "classifier report               precision    recall  f1-score   support\n",
      "\n",
      "       plane     0.7135    0.8120    0.7596      1000\n",
      "         car     0.8459    0.8620    0.8539      1000\n",
      "        bird     0.5716    0.7540    0.6503      1000\n",
      "         cat     0.5686    0.5260    0.5465      1000\n",
      "        deer     0.7914    0.5920    0.6773      1000\n",
      "         dog     0.6022    0.6480    0.6243      1000\n",
      "        frog     0.6968    0.8620    0.7707      1000\n",
      "       horse     0.8964    0.6400    0.7468      1000\n",
      "        ship     0.9192    0.7850    0.8468      1000\n",
      "       truck     0.8351    0.8100    0.8223      1000\n",
      "\n",
      "    accuracy                         0.7291     10000\n",
      "   macro avg     0.7441    0.7291    0.7298     10000\n",
      "weighted avg     0.7441    0.7291    0.7298     10000\n",
      " \n",
      "\n"
     ]
    }
   ],
   "source": [
    "classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse',\n",
    "           'ship', 'truck')\n",
    "print(\"classifier report {} \\n\".\n",
    "              format(metrics.classification_report(y_labels, y_preds, digits=4, target_names=classes)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5d8bdfd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion matrix:\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 800x800 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "\n",
    "cm = confusion_matrix(y_labels, y_preds)\n",
    "print(\"confusion matrix:\")\n",
    "\n",
    "fig = plt.figure(figsize=(8, 8), dpi=100)\n",
    "ax = fig.add_subplot(111)\n",
    "cm_display = ConfusionMatrixDisplay(cm, display_labels=classes).plot(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c804d3d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}